Comparative Analysis of Machine Learning Techniques for Classifying the Risk of Cardiovascular Diseases

Abstract

Heart and blood vessel problems are collectively referred to as cardiovascular diseases. Fatty deposits build up inside an artery, generating a blood clot, which causes the artery to harden and constrict, reducing blood flow to the body, brain, or heart. In the study, a comparative analysis between nine machine learning classifiers has been made. Also, in the study, the authors are training and testing the data set under different split ratio to analyze the difference in the result that the authors obtain after executing those set of data. The split ratio includes a 60:40 split ratio, 70:30 split ratio, 80:20 split ratio, and 90:10 split ratio. They analyzed the performance of the classifiers with respect to various metrics. They concluded by saying the proposed model yields the best accuracy when they use a random forest classifier with an accuracy of 99.26% for the split ratio of 60:40.